Overview

Dataset statistics

Number of variables13
Number of observations935
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.6 KiB
Average record size in memory52.1 B

Variable types

Categorical1
Numeric12

Alerts

step_strip has unique valuesUnique
density_strip has unique valuesUnique
ratio_filler_matrix has unique valuesUnique
density has unique valuesUnique
elasticity_module has unique valuesUnique
number_hardeners has unique valuesUnique
content_epoxy_groups has unique valuesUnique
surface_density has unique valuesUnique
elasticity_module_stretching has unique valuesUnique
strapery_strength has unique valuesUnique
resin_consumption has unique valuesUnique

Reproduction

Analysis started2023-04-10 09:31:41.965349
Analysis finished2023-04-10 09:31:58.381915
Duration16.42 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

pattern_angle
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
1
478 
0
457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters935
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

Length

2023-04-10T12:31:58.494175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-10T12:31:58.588009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

Most occurring characters

ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 935
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 935
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 478
51.1%
0 457
48.9%

step_strip
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9527022
Minimum1.5252299
Maximum13.378772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:58.666125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.5252299
5-th percentile3.1717071
Q15.2086976
median6.9107447
Q38.5292249
95-th percentile11.048011
Maximum13.378772
Range11.853542
Interquartile range (IQR)3.3205273

Descriptive statistics

Standard deviation2.366303
Coefficient of variation (CV)0.34034292
Kurtosis-0.30191979
Mean6.9527022
Median Absolute Deviation (MAD)1.6702967
Skewness0.17731357
Sum6500.7765
Variance5.59939
MonotonicityNot monotonic
2023-04-10T12:31:58.759863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.856166363 1
 
0.1%
9.184196472 1
 
0.1%
8.029073715 1
 
0.1%
9.252672195 1
 
0.1%
5.959359169 1
 
0.1%
9.845168114 1
 
0.1%
7.73243618 1
 
0.1%
3.268975258 1
 
0.1%
6.709406853 1
 
0.1%
10.19870281 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
1.525229931 1
0.1%
1.565613508 1
0.1%
1.598044276 1
0.1%
1.626690269 1
0.1%
1.694136739 1
0.1%
1.700747728 1
0.1%
1.734744072 1
0.1%
1.763323784 1
0.1%
1.970764399 1
0.1%
2.042771816 1
0.1%
ValueCountFrequency (%)
13.37877178 1
0.1%
13.37485504 1
0.1%
13.37103844 1
0.1%
13.24621964 1
0.1%
13.19900703 1
0.1%
12.95363712 1
0.1%
12.8370018 1
0.1%
12.72388649 1
0.1%
12.67892742 1
0.1%
12.65647507 1
0.1%

density_strip
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.445666
Minimum29.74585
Maximum86.012428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:58.869223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum29.74585
5-th percentile37.945484
Q150.266865
median57.205013
Q364.885883
95-th percentile76.475463
Maximum86.012428
Range56.266579
Interquartile range (IQR)14.619019

Descriptive statistics

Standard deviation11.116488
Coefficient of variation (CV)0.19351309
Kurtosis-0.26599032
Mean57.445666
Median Absolute Deviation (MAD)7.317585
Skewness0.010027071
Sum53711.698
Variance123.57632
MonotonicityNot monotonic
2023-04-10T12:31:58.965739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.30196381 1
 
0.1%
65.78954315 1
 
0.1%
59.29868317 1
 
0.1%
57.88181686 1
 
0.1%
49.25912857 1
 
0.1%
57.44395447 1
 
0.1%
57.87047195 1
 
0.1%
67.03882599 1
 
0.1%
52.36651611 1
 
0.1%
63.74240112 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
29.74584961 1
0.1%
29.94907188 1
0.1%
30.88269424 1
0.1%
31.21845055 1
0.1%
31.67418861 1
0.1%
31.79684258 1
0.1%
31.804142 1
0.1%
32.1476593 1
0.1%
32.1954689 1
0.1%
32.76585388 1
0.1%
ValueCountFrequency (%)
86.01242828 1
0.1%
85.98717499 1
0.1%
85.66099548 1
0.1%
84.84088898 1
0.1%
84.66911316 1
0.1%
84.31076813 1
0.1%
84.13278198 1
0.1%
84.01524353 1
0.1%
83.9572525 1
0.1%
82.95615387 1
0.1%

ratio_filler_matrix
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9238492
Minimum0.547391
Maximum5.3141437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:59.072838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.547391
5-th percentile1.4977377
Q12.3287122
median2.9061565
Q33.5510231
95-th percentile4.4423167
Maximum5.3141437
Range4.7667527
Interquartile range (IQR)1.2223109

Descriptive statistics

Standard deviation0.89494026
Coefficient of variation (CV)0.3060829
Kurtosis-0.32916418
Mean2.9238492
Median Absolute Deviation (MAD)0.61815429
Skewness0.045242384
Sum2733.799
Variance0.8009181
MonotonicityNot monotonic
2023-04-10T12:31:59.166584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.587347746 1
 
0.1%
2.549752951 1
 
0.1%
1.042215228 1
 
0.1%
2.985660791 1
 
0.1%
2.163429499 1
 
0.1%
3.043392658 1
 
0.1%
3.905552626 1
 
0.1%
1.735248089 1
 
0.1%
1.827104688 1
 
0.1%
2.64312458 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
0.5473909974 1
0.1%
0.5515084863 1
0.1%
0.5967829227 1
0.1%
0.7492232323 1
0.1%
0.7904994488 1
0.1%
0.827015698 1
0.1%
0.8573807478 1
0.1%
0.8754576445 1
0.1%
0.9278407097 1
0.1%
0.9680865407 1
0.1%
ValueCountFrequency (%)
5.314143658 1
0.1%
5.295842171 1
0.1%
5.210442543 1
0.1%
5.120368481 1
0.1%
5.110780716 1
0.1%
5.098993301 1
0.1%
5.045724392 1
0.1%
5.040265083 1
0.1%
4.944947243 1
0.1%
4.922781467 1
0.1%

density
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1973.6107
Minimum1784.4823
Maximum2160.7515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:59.275938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1784.4823
5-th percentile1854.2084
Q11923.7873
median1976.866
Q32019.5877
95-th percentile2090.7597
Maximum2160.7515
Range376.26917
Interquartile range (IQR)95.800415

Descriptive statistics

Standard deviation70.484146
Coefficient of variation (CV)0.035713298
Kurtosis-0.22807497
Mean1973.6107
Median Absolute Deviation (MAD)48.061157
Skewness-0.0085934056
Sum1845326
Variance4968.0146
MonotonicityNot monotonic
2023-04-10T12:31:59.369687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1953.274902 1
 
0.1%
1895.527832 1
 
0.1%
1866.605469 1
 
0.1%
1977.112671 1
 
0.1%
2025.648193 1
 
0.1%
1915.616211 1
 
0.1%
1911.819458 1
 
0.1%
1986.156494 1
 
0.1%
1985.995728 1
 
0.1%
1988.098999 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
1784.4823 1
0.1%
1786.035645 1
0.1%
1801.940674 1
0.1%
1804.84021 1
0.1%
1805.735962 1
0.1%
1807.595947 1
0.1%
1810.357788 1
0.1%
1810.471191 1
0.1%
1813.234619 1
0.1%
1813.684082 1
0.1%
ValueCountFrequency (%)
2160.751465 1
0.1%
2158.794922 1
0.1%
2150.792969 1
0.1%
2150.337646 1
0.1%
2145.260254 1
0.1%
2145.150146 1
0.1%
2144.523682 1
0.1%
2143.729492 1
0.1%
2142.065918 1
0.1%
2136.038086 1
0.1%

elasticity_module
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744.06119
Minimum53.582279
Maximum1615.0969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:59.492621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum53.582279
5-th percentile240.45124
Q1516.95105
median737.5929
Q3959.44235
95-th percentile1264.361
Maximum1615.0969
Range1561.5146
Interquartile range (IQR)442.4913

Descriptive statistics

Standard deviation313.93512
Coefficient of variation (CV)0.42192111
Kurtosis-0.43513799
Mean744.06119
Median Absolute Deviation (MAD)220.90033
Skewness0.12769578
Sum695697.21
Variance98555.258
MonotonicityNot monotonic
2023-04-10T12:31:59.586513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1136.596191 1
 
0.1%
286.0137329 1
 
0.1%
693.5780029 1
 
0.1%
764.1628418 1
 
0.1%
741.8399048 1
 
0.1%
474.0895996 1
 
0.1%
665.9319458 1
 
0.1%
859.744873 1
 
0.1%
588.9080811 1
 
0.1%
1097.244873 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
53.58227921 1
0.1%
60.47488785 1
0.1%
66.62728882 1
0.1%
69.21657562 1
0.1%
71.61676788 1
0.1%
73.75846863 1
0.1%
78.9143219 1
0.1%
79.67456055 1
0.1%
82.27881622 1
0.1%
97.7337265 1
0.1%
ValueCountFrequency (%)
1615.096924 1
0.1%
1588.677246 1
0.1%
1572.096069 1
0.1%
1546.290894 1
0.1%
1543.023682 1
0.1%
1542.168457 1
0.1%
1501.916016 1
0.1%
1495.726074 1
0.1%
1490.46875 1
0.1%
1490.320068 1
0.1%

number_hardeners
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.32941
Minimum35.599697
Maximum181.82845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:59.711503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum35.599697
5-th percentile63.522323
Q191.677216
median109.90776
Q3130.05563
95-th percentile153.85565
Maximum181.82845
Range146.22875
Interquartile range (IQR)38.378418

Descriptive statistics

Standard deviation27.085167
Coefficient of variation (CV)0.24549363
Kurtosis-0.23497619
Mean110.32941
Median Absolute Deviation (MAD)19.235901
Skewness-0.087799102
Sum103158
Variance733.60626
MonotonicityNot monotonic
2023-04-10T12:31:59.805240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137.6274261 1
 
0.1%
88.01552582 1
 
0.1%
153.5071564 1
 
0.1%
118.7012863 1
 
0.1%
137.9838409 1
 
0.1%
78.36153412 1
 
0.1%
122.8963547 1
 
0.1%
86.96479797 1
 
0.1%
55.35762405 1
 
0.1%
86.51059723 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
35.59969711 1
0.1%
35.62090302 1
0.1%
38.66849899 1
0.1%
40.30480576 1
0.1%
41.88627625 1
0.1%
43.13258362 1
0.1%
43.45455551 1
0.1%
44.30222321 1
0.1%
45.12217712 1
0.1%
45.86312485 1
0.1%
ValueCountFrequency (%)
181.8284454 1
0.1%
181.0328064 1
0.1%
179.6459656 1
0.1%
176.9188385 1
0.1%
173.2543335 1
0.1%
172.5705414 1
0.1%
171.5363312 1
0.1%
170.8922882 1
0.1%
170.3325653 1
0.1%
169.091629 1
0.1%

content_epoxy_groups
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.268169
Minimum16.048979
Maximum28.955095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:31:59.914602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.048979
5-th percentile18.420922
Q120.579868
median22.226454
Q323.985812
95-th percentile26.198113
Maximum28.955095
Range12.906116
Interquartile range (IQR)3.4059439

Descriptive statistics

Standard deviation2.3787799
Coefficient of variation (CV)0.10682422
Kurtosis-0.38543099
Mean22.268169
Median Absolute Deviation (MAD)1.7204132
Skewness0.032266058
Sum20820.738
Variance5.6585941
MonotonicityNot monotonic
2023-04-10T12:32:00.027812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.34453392 1
 
0.1%
21.21404457 1
 
0.1%
18.3094883 1
 
0.1%
19.54516792 1
 
0.1%
21.11852455 1
 
0.1%
25.57662964 1
 
0.1%
23.16970062 1
 
0.1%
20.85025406 1
 
0.1%
22.0559597 1
 
0.1%
23.33746147 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
16.04897881 1
0.1%
16.39159393 1
0.1%
16.66753197 1
0.1%
16.70410728 1
0.1%
16.71543694 1
0.1%
16.84853554 1
0.1%
16.86966896 1
0.1%
16.97708321 1
0.1%
16.99385071 1
0.1%
16.99530411 1
0.1%
ValueCountFrequency (%)
28.95509529 1
0.1%
28.9074707 1
0.1%
28.84890175 1
0.1%
28.62011528 1
0.1%
28.32496834 1
0.1%
27.92084312 1
0.1%
27.81318283 1
0.1%
27.70768166 1
0.1%
27.63374329 1
0.1%
27.52134132 1
0.1%

flash_temperature
Real number (ℝ)

Distinct934
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.81655
Minimum186.50861
Maximum386.06799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:32:00.137172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum186.50861
5-th percentile220.93685
Q1258.50435
median285.94775
Q3313.03479
95-th percentile351.82052
Maximum386.06799
Range199.55939
Interquartile range (IQR)54.530441

Descriptive statistics

Standard deviation39.653004
Coefficient of variation (CV)0.13873586
Kurtosis-0.36729476
Mean285.81655
Median Absolute Deviation (MAD)27.148407
Skewness0.023202959
Sum267238.47
Variance1572.3608
MonotonicityNot monotonic
2023-04-10T12:32:00.231021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.3028259 2
 
0.2%
234.7168884 1
 
0.1%
338.5956726 1
 
0.1%
351.969635 1
 
0.1%
288.9195251 1
 
0.1%
246.4730225 1
 
0.1%
278.4576721 1
 
0.1%
280.8519897 1
 
0.1%
244.8976593 1
 
0.1%
268.1045837 1
 
0.1%
Other values (924) 924
98.8%
ValueCountFrequency (%)
186.508606 1
0.1%
187.5623474 1
0.1%
187.9779663 1
0.1%
188.9186707 1
0.1%
189.2083893 1
0.1%
189.8670807 1
0.1%
193.4311371 1
0.1%
193.8200073 1
0.1%
193.8801575 1
0.1%
195.5345764 1
0.1%
ValueCountFrequency (%)
386.0679932 1
0.1%
385.8947754 1
0.1%
385.6977844 1
0.1%
383.9384155 1
0.1%
382.7597961 1
0.1%
381.8577271 1
0.1%
381.6891174 1
0.1%
380.4528809 1
0.1%
378.482605 1
0.1%
376.9786377 1
0.1%

surface_density
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484.32814
Minimum6.7792554
Maximum1291.3401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:32:00.340378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.7792554
5-th percentile67.845331
Q1269.24969
median456.90805
Q3695.95572
95-th percentile970.69545
Maximum1291.3401
Range1284.5608
Interquartile range (IQR)426.70602

Descriptive statistics

Standard deviation278.1626
Coefficient of variation (CV)0.57432674
Kurtosis-0.5434863
Mean484.32814
Median Absolute Deviation (MAD)210.4373
Skewness0.35393828
Sum452846.81
Variance77374.438
MonotonicityNot monotonic
2023-04-10T12:32:00.449755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555.8934326 1
 
0.1%
489.8395691 1
 
0.1%
426.5042114 1
 
0.1%
521.6829224 1
 
0.1%
220.8658447 1
 
0.1%
218.9490967 1
 
0.1%
284.9047852 1
 
0.1%
315.2346802 1
 
0.1%
1291.340088 1
 
0.1%
274.1543884 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
6.77925539 1
0.1%
6.986692905 1
0.1%
7.09535408 1
0.1%
8.460317612 1
0.1%
9.04620266 1
0.1%
11.29522419 1
0.1%
11.67496109 1
0.1%
12.04048729 1
0.1%
14.3735075 1
0.1%
14.49696445 1
0.1%
ValueCountFrequency (%)
1291.340088 1
0.1%
1288.691895 1
0.1%
1238.47644 1
0.1%
1230.46521 1
0.1%
1227.243042 1
0.1%
1182.326172 1
0.1%
1181.341431 1
0.1%
1177.612671 1
0.1%
1169.880859 1
0.1%
1166.628906 1
0.1%
Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.314498
Minimum66.05275
Maximum80.970963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:32:00.559110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum66.05275
5-th percentile68.605846
Q171.315876
median73.241463
Q375.329891
95-th percentile78.650102
Maximum80.970963
Range14.918213
Interquartile range (IQR)4.0140152

Descriptive statistics

Standard deviation2.9874799
Coefficient of variation (CV)0.040748829
Kurtosis-0.37315634
Mean73.314498
Median Absolute Deviation (MAD)2.0237274
Skewness0.087755032
Sum68549.056
Variance8.9250364
MonotonicityNot monotonic
2023-04-10T12:32:00.824939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.80322266 1
 
0.1%
72.25097656 1
 
0.1%
70.1084137 1
 
0.1%
74.92824554 1
 
0.1%
71.772789 1
 
0.1%
74.64994049 1
 
0.1%
75.25041199 1
 
0.1%
75.37363434 1
 
0.1%
71.88611603 1
 
0.1%
73.89769745 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
66.05274963 1
0.1%
66.22123718 1
0.1%
66.26528931 1
0.1%
66.42079163 1
0.1%
66.42106628 1
0.1%
66.48238373 1
0.1%
66.52217865 1
0.1%
66.57862091 1
0.1%
66.59835815 1
0.1%
66.73384857 1
0.1%
ValueCountFrequency (%)
80.97096252 1
0.1%
80.80322266 1
0.1%
80.6914978 1
0.1%
80.41986084 1
0.1%
80.38492584 1
0.1%
80.25445557 1
0.1%
80.23960876 1
0.1%
80.14770508 1
0.1%
80.11426544 1
0.1%
80.07536316 1
0.1%

strapery_strength
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2457.6885
Minimum1337.4247
Maximum3579.2656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:32:00.934514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1337.4247
5-th percentile1726.2728
Q12153.4142
median2450.4917
Q32748.2419
95-th percentile3224.2796
Maximum3579.2656
Range2241.8409
Interquartile range (IQR)594.82776

Descriptive statistics

Standard deviation438.72153
Coefficient of variation (CV)0.17850982
Kurtosis-0.29566175
Mean2457.6885
Median Absolute Deviation (MAD)297.91406
Skewness0.049428016
Sum2297938.7
Variance192476.58
MonotonicityNot monotonic
2023-04-10T12:32:01.041592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2587.343018 1
 
0.1%
2724.800781 1
 
0.1%
2461.455078 1
 
0.1%
1955.437866 1
 
0.1%
2067.186035 1
 
0.1%
2940.576416 1
 
0.1%
2121.639404 1
 
0.1%
1677.73938 1
 
0.1%
2675.143066 1
 
0.1%
2339.869629 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
1337.424683 1
0.1%
1337.748291 1
0.1%
1348.288208 1
0.1%
1386.578979 1
0.1%
1388.849365 1
0.1%
1399.11853 1
0.1%
1400.35791 1
0.1%
1446.958008 1
0.1%
1458.432861 1
0.1%
1466.03064 1
0.1%
ValueCountFrequency (%)
3579.265625 1
0.1%
3575.590332 1
0.1%
3548.054688 1
0.1%
3533.008545 1
0.1%
3524.436035 1
0.1%
3520.741943 1
0.1%
3495.23999 1
0.1%
3466.096924 1
0.1%
3456.540771 1
0.1%
3442.970459 1
0.1%

resin_consumption
Real number (ℝ)

Distinct935
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.22832
Minimum82.58316
Maximum359.05222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-04-10T12:32:01.135330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum82.58316
5-th percentile127.11766
Q1179.99666
median216.47929
Q3256.25281
95-th percentile311.0852
Maximum359.05222
Range276.46906
Interquartile range (IQR)76.256149

Descriptive statistics

Standard deviation56.170124
Coefficient of variation (CV)0.25739154
Kurtosis-0.46139348
Mean218.22832
Median Absolute Deviation (MAD)38.413147
Skewness0.054278355
Sum204043.48
Variance3155.083
MonotonicityNot monotonic
2023-04-10T12:32:01.229074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
246.6131134 1
 
0.1%
121.8292542 1
 
0.1%
147.8318787 1
 
0.1%
291.1253662 1
 
0.1%
265.0967407 1
 
0.1%
226.9944611 1
 
0.1%
220.7512512 1
 
0.1%
246.1768188 1
 
0.1%
149.3761292 1
 
0.1%
243.5792236 1
 
0.1%
Other values (925) 925
98.9%
ValueCountFrequency (%)
82.5831604 1
0.1%
84.49012756 1
0.1%
86.56951904 1
0.1%
87.2701416 1
0.1%
87.79727936 1
0.1%
90.50930023 1
0.1%
91.04764557 1
0.1%
92.30715942 1
0.1%
92.44786835 1
0.1%
93.82215881 1
0.1%
ValueCountFrequency (%)
359.0522156 1
0.1%
356.9259644 1
0.1%
355.7586975 1
0.1%
354.9580994 1
0.1%
350.6721191 1
0.1%
350.3608398 1
0.1%
347.9953918 1
0.1%
347.3127136 1
0.1%
346.2761841 1
0.1%
344.243988 1
0.1%

Interactions

2023-04-10T12:31:56.877078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.049368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.228269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.494409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.670149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.986124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.248127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.477357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.830257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.127145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.328240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.575612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.979009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.142422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.405537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.583441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.765802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.076980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.342772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.561545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.932679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.234203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.426087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.668817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.062077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.242627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.494842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.677146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.860542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.178163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.443853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.659419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.047912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.332342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.525787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.767878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.166312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.342539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.592642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.776747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.961661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.284779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.528221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.760489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.142511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.424445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.627056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.859965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.267599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.445390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.697367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.877670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.060417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.392785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.650147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.858858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.243200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.527267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.727063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.944358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.375227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.543356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.802080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.984089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.174380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.493321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.743768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.962146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.368437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.626007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.850573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.199749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.477597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.649249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.898880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.077683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.276150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.613829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.844907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.076130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.484878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.726632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.959345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.301318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.576624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.742296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.994849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.177211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.471095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.720086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.963291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.282121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.594280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.827178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.060001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.401570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.679890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.843669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.100520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.282869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.574833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.825350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.066784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.408756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.708818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.945700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.161089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.499725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.776711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:43.942323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.192762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.376049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.676829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:48.934153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.160559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.509972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.826270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.043764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.260045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.596838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.876631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.045214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.307950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.484622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.776051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.045827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.281562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.617702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:52.943170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.143360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.378682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.693364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:57.978010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:44.127964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:45.399405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:46.560430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:47.877213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:49.143788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:50.376082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:51.730473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:53.027124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:54.226664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:55.480239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-10T12:31:56.790934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-10T12:32:01.322811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
step_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumptionpattern_angle
step_strip1.0000.0160.008-0.0460.008-0.0310.006-0.013-0.0290.029-0.055-0.0040.123
density_strip0.0161.0000.0350.017-0.0070.017-0.047-0.0060.0180.015-0.040-0.0140.359
ratio_filler_matrix0.0080.0351.0000.0030.045-0.0060.016-0.008-0.005-0.0160.0190.0590.045
density-0.0460.0170.0031.000-0.018-0.029-0.006-0.0140.072-0.025-0.080-0.0440.000
elasticity_module0.008-0.0070.045-0.0181.0000.0460.0060.042-0.030-0.0180.0460.0060.026
number_hardeners-0.0310.017-0.006-0.0290.0461.0000.0040.0730.037-0.083-0.047-0.0090.000
content_epoxy_groups0.006-0.0470.016-0.0060.0060.0041.000-0.011-0.0140.044-0.0120.0030.000
flash_temperature-0.013-0.006-0.008-0.0140.0420.073-0.0111.0000.0210.012-0.0260.0430.000
surface_density-0.0290.018-0.0050.072-0.0300.037-0.0140.0211.0000.006-0.000-0.0350.000
elasticity_module_stretching0.0290.015-0.016-0.025-0.018-0.0830.0440.0120.0061.0000.0340.0490.000
strapery_strength-0.055-0.0400.019-0.0800.046-0.047-0.012-0.026-0.0000.0341.0000.0100.050
resin_consumption-0.004-0.0140.059-0.0440.006-0.0090.0030.043-0.0350.0490.0101.0000.000
pattern_angle0.1230.3590.0450.0000.0260.0000.0000.0000.0000.0000.0500.0001.000

Missing values

2023-04-10T12:31:58.107975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-10T12:31:58.300606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pattern_anglestep_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumption
007.85616664.3019642.5873481953.2749021136.596191137.62742622.344534234.716888555.89343380.8032232587.343018246.613113
106.67578078.6232992.0464712037.631836707.570862101.61724923.146393312.307220547.60119673.8170702624.026367178.198563
207.52639838.1769751.8564762018.220337836.294373135.40170326.435514327.510376150.96144177.2107622473.187256123.344559
308.32569946.0454293.3055351917.907471478.286255105.78692617.874100328.154572526.69213972.3457113059.032959275.575867
407.65621133.5710222.7095541892.071167641.05255196.56329322.989290262.956726804.59259074.5113602288.967285126.816338
5010.30294539.2342802.2828252008.357544393.967316149.37283321.661751330.498627535.37146072.2449262704.445068261.077057
608.94689172.0845951.9781401973.629150991.724121149.37213119.750578232.058197485.45376675.6657032448.943115162.493698
703.74662557.9977721.7714361872.491577801.03387579.79454822.296303340.736908864.92919970.9475942796.785400123.356262
809.09436344.8016013.2770872010.046997339.55041567.49899324.280609254.949081117.53523367.4787062462.605469207.018585
906.30377372.1520162.9843621912.3154301183.091797133.54899623.263796314.996124377.38900875.2904512303.770752200.580246
pattern_anglestep_stripdensity_stripratio_filler_matrixdensityelasticity_modulenumber_hardenerscontent_epoxy_groupsflash_temperaturesurface_densityelasticity_module_stretchingstrapery_strengthresin_consumption
92513.48951258.8280372.3103941931.146851554.01031596.74977922.146486214.82772856.24276078.1436081939.30749587.270142
92618.21203252.0738071.6462352014.772583841.064819102.97990421.073366271.490845615.16815279.1544722518.516113232.428207
92717.20150361.0970042.8065631872.864624996.018677146.19918821.559290313.900482799.63409472.8155522443.482910307.265167
92818.43015868.3557973.7458621914.629395680.683716110.97910325.922634309.796387628.36456376.0305562466.925537152.184723
92916.71136454.0594412.7587282000.506104934.564392143.02186621.379519273.85269265.10596567.6337513102.539551229.780365
93018.08811147.7591782.2713461952.087891912.85553086.99218020.123249324.774567209.19870073.0909582387.292480125.007668
93117.61913866.9319313.4440222050.089111444.732635145.98197919.599770254.215408350.66082872.9208302360.392822117.730095
93219.80092572.8582843.2806041972.372925416.836517110.53347823.957502248.423050740.14276174.7343442662.906006236.606766
933110.07985965.5194783.7053512066.799805741.475525141.39796419.246944275.779846641.46814074.0427092071.715820197.126068
93419.02104366.9201433.8080201890.413452417.316223129.18341127.474764300.952698758.74786474.3097082856.328857194.754349